Explainable AI: A Review of Machine Learning Interpretability Methods.
Explainable AI: A Review of Machine Learning Interpretability Methods.
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DOI：
10.3390/e23010018
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发表时间：
2020-12-25
期刊：
影响因子：
--
通讯作者：
Kotsiantis S
中科院分区：
文献类型：
--
作者：
Linardatos P;Papastefanopoulos V;Kotsiantis S
人工智能 (AI) 的最新进展导致其在工业领域得到广泛应用，机器学习系统在大量任务中展现出超人的表现。然而，性能的激增通常是通过增加模型复杂性来实现的，将此类系统变成“黑匣子”方法，并导致其运行方式以及最终决策方式的不确定性。这种模糊性使得机器学习系统在敏感但关键的领域中采用时遇到了问题，这些领域的价值可能是巨大的，例如医疗保健。因此，近年来，人们对可解释人工智能（XAI）领域的科学兴趣重新燃起，该领域涉及开发解释和解释机器学习模型的新方法。本研究重点关注机器学习的可解释性方法；更具体地说，提出了这些方法的文献综述和分类，以及它们的编程实现的链接，希望这项调查能够为理论家和实践者提供参考。
Recent advances in artificial intelligence (AI) have led to its widespread industrial adoption, with machine learning systems demonstrating superhuman performance in a significant number of tasks. However, this surge in performance, has often been achieved through increased model complexity, turning such systems into “black box” approaches and causing uncertainty regarding the way they operate and, ultimately, the way that they come to decisions. This ambiguity has made it problematic for machine learning systems to be adopted in sensitive yet critical domains, where their value could be immense, such as healthcare. As a result, scientific interest in the field of Explainable Artificial Intelligence (XAI), a field that is concerned with the development of new methods that explain and interpret machine learning models, has been tremendously reignited over recent years. This study focuses on machine learning interpretability methods; more specifically, a literature review and taxonomy of these methods are presented, as well as links to their programming implementations, in the hope that this survey would serve as a reference point for both theorists and practitioners.
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